Pandora Papers 2021 leak of financial documents We ask you, humbly: don't scroll away. Hi reader, this Saturday, for the 2nd time recently, we ask you to protect Wikipedia's independence. Thanks to the 2% of readers who donate, Wikipedia and the free knowledge movement are thriving. If you too have benefitted from using Wikipedia, take a minute to donate to keep it growing for years. If you are one of our rare donors, we warmly thank you. Thank you! Symbol used to represent the leak by the International Consortium of Investigative Journalists The Pandora Papers are 11.9 million leaked documents with 2.9 terabytes of data that the International Consortium of Investigative Journalists (ICIJ) published beginning on 3 October 2021.[1][2][3] The leak exposed the secret offshore accounts of 35 world leaders, including current and former presidents, prime ministers, and heads of state as well as more than 100 billionaires, celebrities, and business leaders. Disclosures[edit] Data sources[edit] [edit] Africa[edit]
National Open and Distance University UNAD - Panama Papers 2016 document leak scandal Countries with politicians, public officials or close associates implicated in the leak on April 15, 2016 (as of May 19, 2016) The Panama Papers (Spanish: Papeles de Panamá) are 11.5 million leaked documents (or 2.6 terabytes of data) that were published beginning on April 3, 2016. The papers detail financial and attorney–client information for more than 214,488 offshore entities.[1][2][3][4][5][6] The documents, some dating back to the 1970s,[7] were created by, and taken from, former Panamanian offshore law firm and corporate service provider Mossack Fonseca.[8][9][10] SZ asked the ICIJ for help because of the amount of data involved. In October 2020, German authorities issued an international arrest warrant for the two founders of the law firm at the core of the tax evasion scandal exposed by the Panama Papers. Disclosures[edit] The journalists on the investigative team found business transactions by many important figures in world politics, sports, and art.
Blog: Natural Language Processing with R Programming Books Natural Language Processing is a key Data Science skill. Learn how to can expand your R programming knowledge with Text Analytics. It is my firm conviction that Natural Language Processing/Text Analytics is a must-have skill for any practicing Data Scientist. Not surprisingly, I am often asked by students of our Bootcamp, folks that I mentor on Data Science and my LinkedIn contacts about the subject of Text Analytics. Text Analytics with R for Students of Literature is quite simply the best, most straightforward introduction to working with text that I have found. Taming Text is the next stop on the Text Analytics journey. The CRAN NLP Task View illustrates the wide-ranging Text Analytics support for the R programmer . Introduction to Information Retrieval for Text Analytics while focused primarily on the problem of search, nevertheless contains a wealth of theory and understanding (e.g., the Vector Space Model) to take the R programmer to the next level.
fulltext manual An R package to search across and get full text for journal articles The fulltext package makes it easy to do text-mining by supporting the following steps: Search for articlesFetch articlesGet links for full text articles (xml, pdf)Extract text from articles / convert formatsCollect bits of articles that you actually needDownload supplementary materials from papers Citing fulltext Scott Chamberlain (2019). fulltext: Full Text of ‘Scholarly’ Articles Across Many Data Sources. Installation Stable version from CRAN install.packages("fulltext") Development version from GitHub remotes::install_github("ropensci/fulltext") Load library
Text Analysis with R It is recommended that you not only intall, but also load the packages, to make sure the respective versions get along with your R version. Feinerer, I., Hornik, K., and Meyer, D. (2008). Text Mining Infrastructure in R. Journal of Statistical Software, 25(5), 1 - 54. doi: Gries, Stefan Thomas, 2009: Quantitative Corpus Linguistics with R: A Practical Introduction. Silge, J and D. Kasper Welbers, Wouter Van Atteveldt & Kenneth Benoit (2017) Text Analysis in R, Communication Methods and Measures, 11:4, 245-265, DOI: 10.1080/19312458.2017.1387238 Scott Chamberlain (2019). fulltext: Full Text of ‘Scholarly’ Articles Across Many Data Sources CRAN Task View: Natural Language Processing
Wikileaks: a diez años del sismo político del Cablegate, EE. UU. sigue en la mira | El Mundo | DW | 27.11.2020 La noticia cayó como un rayo el 28 de noviembre de 2010. Cinco importantes medios occidentales comenzaron a publicar simultáneamente secretos de la sala de máquinas de la diplomacia de Washington. El material: exactamente 251.287 documentos, en su mayoría secretos y confidenciales, del Departamento de Estado de la superpotencia, que ofrecían una imagen sin adornos de la política exterior estadounidense en documentos provenientes de embajadas estadounidenses en todo el mundo. La plataforma Wikileaks los hizo accesibles. El socio alemán de Wikileaks fue la revista Der Spiegel, que se refirió a una "catástrofe mayúscula” para la política exterior de Estados Unidos". De "asesinato colateral" a Cablegate "Desde nuestro punto de vista, los despachos de la embajada fueron un punto culminante de las revelaciones de Wikileaks en 2010", recuerda el periodista de Spiegel Marcel Rosenbach en conversación con DW. Chelsea Manning. Las chispas de la primavera árabe Estados Unidos contra Julian Assange
MonkeyLearn - Text Mining: The Beginner's Guide What is Text Mining? Text mining, also known as text analysis, is the process of transforming unstructured text data into meaningful and actionable information. Text mining utilizes different AI technologies to automatically process data and generate valuable insights, enabling companies to make data-driven decisions. For businesses, the large amount of data generated every day represents both an opportunity and a challenge. Like most things related to Natural Language Processing (NLP), text mining may sound like a hard-to-grasp concept. Let’s jump right into it! Getting Started With Text Mining Text mining is an automatic process that uses natural language processing to extract valuable insights from unstructured text. Thanks to text mining, businesses are being able to analyze complex and large sets of data in a simple, fast and effective way. Let’s say you need to examine tons of reviews in G2 Crowd to understand what customers are praising or criticizing about your SaaS. Basic Methods
Categorización de conflictos sociales en el ámbito de los recursos naturales: un estudio de las actividades extractivas mediante la minería de textos | Publicación Mediante la aplicación de técnicas de minería de textos, se desarrolló una metodología para medir el número de conflictos sociales relacionados con la explotación de recursos naturales no renovables. Este estudio se centra en los conflictos de cuatro países mineros (Australia, Canadá, Chile y Perú) entre 2003 y 2016, sobre la base de más de 20.000 artículos de los principales periódicos de cada país. Se halló una correlación estadísticamente significativa entre el principal índice y las rentas procedentes de la minería como porcentaje del producto interno bruto (PIB). No obstante, estos resultados deben interpretarse con cautela, dado que no se han abordado los problemas de endogeneidad, y los índices podrían presentar sesgos a causa de diversos factores específicos de cada país.
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